Overview

Dataset statistics

Number of variables16
Number of observations1209
Missing cells855
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.2 KiB
Average record size in memory128.1 B

Variable types

DateTime1
Numeric15

Alerts

Consumption is highly correlated with Combined Cycle Generation and 2 other fieldsHigh correlation
Wind Generation is highly correlated with Combined Cycle Generation and 1 other fieldsHigh correlation
Combined Cycle Generation is highly correlated with Consumption and 3 other fieldsHigh correlation
Total emissions (tCO2eq) is highly correlated with Consumption and 3 other fieldsHigh correlation
tCO2/MWh is highly correlated with Wind Generation and 3 other fieldsHigh correlation
Electricity Price is highly correlated with Solar Generation and 5 other fieldsHigh correlation
Electricity Price 24h is highly correlated with Solar Generation and 5 other fieldsHigh correlation
Electricity Price 7d is highly correlated with Solar Generation and 5 other fieldsHigh correlation
Gas Price is highly correlated with Consumption and 8 other fieldsHigh correlation
Gas Price 24h is highly correlated with Solar Generation and 5 other fieldsHigh correlation
Gas Price 7d is highly correlated with Solar Generation and 5 other fieldsHigh correlation
Solar Generation is highly correlated with Electricity Price and 4 other fieldsHigh correlation
Hydropower Generation is highly correlated with Gas PriceHigh correlation
Gas Price has 851 (70.4%) missing values Missing
datetime has unique values Unique
Consumption has unique values Unique
Wind Generation has unique values Unique
Hydropower Generation has unique values Unique
Combined Cycle Generation has unique values Unique
Total emissions (tCO2eq) has unique values Unique

Reproduction

Analysis started2022-12-19 11:41:49.375553
Analysis finished2022-12-19 11:42:04.604086
Duration15.23 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

datetime
Date

UNIQUE

Distinct1209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
Minimum2019-01-08 00:00:00
Maximum2022-04-30 00:00:00
2022-12-19T12:42:04.682224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:04.754171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Consumption
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666266.5045
Minimum474176
Maximum846631.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:04.825441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum474176
5-th percentile545202.48
Q1618178.6
median673593
Q3713659.3
95-th percentile771092.9
Maximum846631.4
Range372455.4
Interquartile range (IQR)95480.7

Descriptive statistics

Standard deviation69030.9016
Coefficient of variation (CV)0.1036085427
Kurtosis-0.2628945034
Mean666266.5045
Median Absolute Deviation (MAD)45223.3
Skewness-0.2474858152
Sum805516203.9
Variance4765265376
MonotonicityNot monotonic
2022-12-19T12:42:04.895552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
805391.71
 
0.1%
664061.61
 
0.1%
543850.31
 
0.1%
618570.71
 
0.1%
684346.61
 
0.1%
690482.21
 
0.1%
701831.11
 
0.1%
708593.81
 
0.1%
701923.41
 
0.1%
600075.31
 
0.1%
Other values (1199)1199
99.2%
ValueCountFrequency (%)
4741761
0.1%
482228.51
0.1%
482767.11
0.1%
486024.51
0.1%
487247.51
0.1%
4892141
0.1%
4895321
0.1%
491226.81
0.1%
493723.71
0.1%
493821.61
0.1%
ValueCountFrequency (%)
846631.41
0.1%
845359.51
0.1%
837014.41
0.1%
835205.71
0.1%
828296.81
0.1%
8266711
0.1%
826580.21
0.1%
824726.71
0.1%
821234.91
0.1%
817856.61
0.1%

Solar Generation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1204
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42491.70033
Minimum5663.4
Maximum108105.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:04.972241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5663.4
5-th percentile14431.48
Q126827.8
median36803.2
Q357777.3
95-th percentile82366.32
Maximum108105.9
Range102442.5
Interquartile range (IQR)30949.5

Descriptive statistics

Standard deviation20727.13702
Coefficient of variation (CV)0.4877926009
Kurtosis-0.4538602291
Mean42491.70033
Median Absolute Deviation (MAD)14726
Skewness0.5872737718
Sum51372465.7
Variance429614209.1
MonotonicityNot monotonic
2022-12-19T12:42:05.048401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50507.62
 
0.2%
21719.82
 
0.2%
53160.22
 
0.2%
32382.22
 
0.2%
49986.92
 
0.2%
16757.91
 
0.1%
61849.71
 
0.1%
619861
 
0.1%
58769.91
 
0.1%
68722.31
 
0.1%
Other values (1194)1194
98.8%
ValueCountFrequency (%)
5663.41
0.1%
5998.61
0.1%
7959.71
0.1%
81051
0.1%
8151.61
0.1%
8155.11
0.1%
8691.31
0.1%
8824.11
0.1%
9267.41
0.1%
9787.51
0.1%
ValueCountFrequency (%)
108105.91
0.1%
106380.21
0.1%
1054981
0.1%
100920.41
0.1%
100820.61
0.1%
1006761
0.1%
100440.91
0.1%
99727.51
0.1%
99725.91
0.1%
97668.41
0.1%

Wind Generation
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155669.7677
Minimum25824.1
Maximum430110.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:05.125582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum25824.1
5-th percentile53438.32
Q191220.5
median136342
Q3206236.6
95-th percentile320658.26
Maximum430110.2
Range404286.1
Interquartile range (IQR)115016.1

Descriptive statistics

Standard deviation83004.27133
Coefficient of variation (CV)0.5332073949
Kurtosis0.1601816149
Mean155669.7677
Median Absolute Deviation (MAD)52201.7
Skewness0.8612450084
Sum188204749.2
Variance6889709059
MonotonicityNot monotonic
2022-12-19T12:42:05.204683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171527.71
 
0.1%
205068.61
 
0.1%
125528.41
 
0.1%
1484601
 
0.1%
104912.91
 
0.1%
50030.91
 
0.1%
32119.71
 
0.1%
65947.51
 
0.1%
229113.51
 
0.1%
289298.61
 
0.1%
Other values (1199)1199
99.2%
ValueCountFrequency (%)
25824.11
0.1%
26449.71
0.1%
274171
0.1%
29366.41
0.1%
29444.31
0.1%
32000.71
0.1%
32119.71
0.1%
342881
0.1%
35702.71
0.1%
36331.31
0.1%
ValueCountFrequency (%)
430110.21
0.1%
417646.91
0.1%
409576.31
0.1%
4081831
0.1%
406145.41
0.1%
405210.91
0.1%
4038221
0.1%
398397.81
0.1%
392107.41
0.1%
388627.21
0.1%

Hydropower Generation
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60316.2225
Minimum12400.8
Maximum163246.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:05.284612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12400.8
5-th percentile24930.14
Q138598.6
median52868.7
Q375587.7
95-th percentile127081.06
Maximum163246.4
Range150845.6
Interquartile range (IQR)36989.1

Descriptive statistics

Standard deviation30121.95488
Coefficient of variation (CV)0.4994005531
Kurtosis0.8917708587
Mean60316.2225
Median Absolute Deviation (MAD)16782.4
Skewness1.13751033
Sum72922313
Variance907332165.6
MonotonicityNot monotonic
2022-12-19T12:42:05.355473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55914.41
 
0.1%
86840.71
 
0.1%
55104.71
 
0.1%
74043.31
 
0.1%
1119191
 
0.1%
126308.21
 
0.1%
127711.81
 
0.1%
1133491
 
0.1%
93343.41
 
0.1%
43370.51
 
0.1%
Other values (1199)1199
99.2%
ValueCountFrequency (%)
12400.81
0.1%
13277.81
0.1%
14106.71
0.1%
15333.91
0.1%
16484.21
0.1%
16643.21
0.1%
16648.61
0.1%
16756.71
0.1%
16907.21
0.1%
17129.31
0.1%
ValueCountFrequency (%)
163246.41
0.1%
159710.51
0.1%
159612.11
0.1%
154032.11
0.1%
153396.11
0.1%
1528271
0.1%
152813.51
0.1%
152228.41
0.1%
151723.21
0.1%
151723.11
0.1%

Pump Hydro Generation
Real number (ℝ≥0)

Distinct1194
Distinct (%)99.1%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6152.003402
Minimum9.7
Maximum28965.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:05.431047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.7
5-th percentile601.24
Q12294.7
median4940.2
Q38848.1
95-th percentile15890.52
Maximum28965.2
Range28955.5
Interquartile range (IQR)6553.4

Descriptive statistics

Standard deviation4920.980421
Coefficient of variation (CV)0.7998988458
Kurtosis1.494432405
Mean6152.003402
Median Absolute Deviation (MAD)3075.8
Skewness1.200912232
Sum7413164.1
Variance24216048.3
MonotonicityNot monotonic
2022-12-19T12:42:05.499447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2772.22
 
0.2%
2522.52
 
0.2%
5373.22
 
0.2%
7542.42
 
0.2%
2109.12
 
0.2%
7318.32
 
0.2%
881.82
 
0.2%
11569.12
 
0.2%
222312
 
0.2%
24957.62
 
0.2%
Other values (1184)1185
98.0%
(Missing)4
 
0.3%
ValueCountFrequency (%)
9.71
0.1%
35.21
0.1%
411
0.1%
70.91
0.1%
72.61
0.1%
731
0.1%
80.11
0.1%
881
0.1%
891
0.1%
96.71
0.1%
ValueCountFrequency (%)
28965.21
0.1%
26297.11
0.1%
25624.41
0.1%
24957.62
0.2%
23961.61
0.1%
23954.41
0.1%
22948.71
0.1%
22479.41
0.1%
22469.61
0.1%
22274.81
0.1%

Combined Cycle Generation
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118163.0775
Minimum16887.7
Maximum370914.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:05.570888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16887.7
5-th percentile32118.68
Q164153.8
median105450.6
Q3162177.8
95-th percentile243796.16
Maximum370914.9
Range354027.2
Interquartile range (IQR)98024

Descriptive statistics

Standard deviation67244.51604
Coefficient of variation (CV)0.569082301
Kurtosis-0.1457917245
Mean118163.0775
Median Absolute Deviation (MAD)47942.4
Skewness0.7004438164
Sum142859160.7
Variance4521824937
MonotonicityNot monotonic
2022-12-19T12:42:05.640275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120908.11
 
0.1%
46997.31
 
0.1%
41717.11
 
0.1%
45682.51
 
0.1%
74449.51
 
0.1%
90970.21
 
0.1%
92751.21
 
0.1%
99634.71
 
0.1%
53433.51
 
0.1%
27016.11
 
0.1%
Other values (1199)1199
99.2%
ValueCountFrequency (%)
16887.71
0.1%
17502.31
0.1%
17578.81
0.1%
17620.51
0.1%
18928.81
0.1%
19164.11
0.1%
19457.41
0.1%
19699.81
0.1%
20032.31
0.1%
20336.61
0.1%
ValueCountFrequency (%)
370914.91
0.1%
327746.71
0.1%
320531.81
0.1%
315803.71
0.1%
315329.81
0.1%
314177.31
0.1%
311919.51
0.1%
311091.51
0.1%
300785.61
0.1%
2996731
0.1%

Nuclear Generation
Real number (ℝ≥0)

Distinct1201
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152036.1632
Minimum90283.3
Maximum170911.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:05.714422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum90283.3
5-th percentile105512.96
Q1142710
median165590.4
Q3169476.8
95-th percentile170591.34
Maximum170911.7
Range80628.4
Interquartile range (IQR)26766.8

Descriptive statistics

Standard deviation21372.69546
Coefficient of variation (CV)0.1405763932
Kurtosis0.1964844081
Mean152036.1632
Median Absolute Deviation (MAD)5020.9
Skewness-1.09948153
Sum183811721.3
Variance456792111.3
MonotonicityNot monotonic
2022-12-19T12:42:05.785597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166621.83
 
0.2%
170453.22
 
0.2%
170754.22
 
0.2%
166366.82
 
0.2%
1197482
 
0.2%
166607.62
 
0.2%
170360.42
 
0.2%
146416.41
 
0.1%
146425.21
 
0.1%
146449.21
 
0.1%
Other values (1191)1191
98.5%
ValueCountFrequency (%)
90283.31
0.1%
90347.31
0.1%
90362.41
0.1%
90539.71
0.1%
91573.11
0.1%
91663.21
0.1%
91863.61
0.1%
92010.31
0.1%
920311
0.1%
92277.61
0.1%
ValueCountFrequency (%)
170911.71
0.1%
170882.91
0.1%
170859.11
0.1%
170854.11
0.1%
170843.41
0.1%
170841.71
0.1%
170840.21
0.1%
170835.21
0.1%
170834.21
0.1%
170815.81
0.1%

Total emissions (tCO2eq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1209
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103360.9096
Minimum42214.689
Maximum269859.051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:05.859540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum42214.689
5-th percentile55193.9962
Q174132.571
median96719.783
Q3124557.649
95-th percentile175192.739
Maximum269859.051
Range227644.362
Interquartile range (IQR)50425.078

Descriptive statistics

Standard deviation37866.22371
Coefficient of variation (CV)0.3663495594
Kurtosis1.054798152
Mean103360.9096
Median Absolute Deviation (MAD)24532.851
Skewness0.9691845456
Sum124963339.8
Variance1433850898
MonotonicityNot monotonic
2022-12-19T12:42:05.934443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221495.4911
 
0.1%
64932.3181
 
0.1%
55283.3431
 
0.1%
62236.1121
 
0.1%
76755.4731
 
0.1%
83707.0121
 
0.1%
83694.3471
 
0.1%
85357.851
 
0.1%
67637.6231
 
0.1%
49585.7221
 
0.1%
Other values (1199)1199
99.2%
ValueCountFrequency (%)
42214.6891
0.1%
42995.1241
0.1%
45334.3681
0.1%
46521.6371
0.1%
46674.9571
0.1%
46998.6081
0.1%
47427.6661
0.1%
47739.261
0.1%
47975.8591
0.1%
48156.0651
0.1%
ValueCountFrequency (%)
269859.0511
0.1%
268388.1161
0.1%
265329.5031
0.1%
258317.3611
0.1%
242400.3031
0.1%
237737.0571
0.1%
229900.0381
0.1%
229509.4411
0.1%
226454.6821
0.1%
226088.161
0.1%

tCO2/MWh
Real number (ℝ≥0)

HIGH CORRELATION

Distinct208
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1583316791
Minimum0.066
Maximum0.332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:06.013101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.066
5-th percentile0.088
Q10.124
median0.155
Q30.188
95-th percentile0.237
Maximum0.332
Range0.266
Interquartile range (IQR)0.064

Descriptive statistics

Standard deviation0.04593039289
Coefficient of variation (CV)0.2900897228
Kurtosis0.06589147726
Mean0.1583316791
Median Absolute Deviation (MAD)0.032
Skewness0.4332925895
Sum191.423
Variance0.002109600991
MonotonicityNot monotonic
2022-12-19T12:42:06.105345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18417
 
1.4%
0.15917
 
1.4%
0.17916
 
1.3%
0.14616
 
1.3%
0.13816
 
1.3%
0.12715
 
1.2%
0.15614
 
1.2%
0.15413
 
1.1%
0.12113
 
1.1%
0.12413
 
1.1%
Other values (198)1059
87.6%
ValueCountFrequency (%)
0.0662
0.2%
0.0671
 
0.1%
0.0694
0.3%
0.071
 
0.1%
0.0712
0.2%
0.0721
 
0.1%
0.0733
0.2%
0.0742
0.2%
0.0751
 
0.1%
0.0761
 
0.1%
ValueCountFrequency (%)
0.3321
0.1%
0.331
0.1%
0.3291
0.1%
0.3251
0.1%
0.3081
0.1%
0.3071
0.1%
0.2941
0.1%
0.2911
0.1%
0.2891
0.1%
0.2811
0.1%

Electricity Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1065
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.59485525
Minimum0.51
Maximum510.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:06.252069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile20.7
Q136.82
median49.76
Q390.24
95-th percentile240.232
Maximum510.26
Range509.75
Interquartile range (IQR)53.42

Descriptive statistics

Standard deviation74.28813572
Coefficient of variation (CV)0.9217478645
Kurtosis3.354340898
Mean80.59485525
Median Absolute Deviation (MAD)16.66
Skewness1.867278574
Sum97439.18
Variance5518.727108
MonotonicityNot monotonic
2022-12-19T12:42:06.338618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.956
 
0.5%
406
 
0.5%
375
 
0.4%
454
 
0.3%
304
 
0.3%
39.074
 
0.3%
514
 
0.3%
603
 
0.2%
42.073
 
0.2%
93.353
 
0.2%
Other values (1055)1167
96.5%
ValueCountFrequency (%)
0.511
 
0.1%
1.941
 
0.1%
1.956
0.5%
2.381
 
0.1%
3.51
 
0.1%
4.641
 
0.1%
4.721
 
0.1%
5.021
 
0.1%
5.51
 
0.1%
5.951
 
0.1%
ValueCountFrequency (%)
510.261
0.1%
450.951
0.1%
441.851
0.1%
390.611
0.1%
386.11
0.1%
3781
0.1%
376.51
0.1%
354.491
0.1%
3501
0.1%
343.081
0.1%

Electricity Price 24h
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1065
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.44333333
Minimum0.51
Maximum510.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:06.448588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile20.7
Q136.82
median49.86
Q389.8
95-th percentile240.232
Maximum510.26
Range509.75
Interquartile range (IQR)52.98

Descriptive statistics

Standard deviation74.21130924
Coefficient of variation (CV)0.9225290172
Kurtosis3.396172698
Mean80.44333333
Median Absolute Deviation (MAD)16.74
Skewness1.876916279
Sum97255.99
Variance5507.318419
MonotonicityNot monotonic
2022-12-19T12:42:06.554659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
406
 
0.5%
1.956
 
0.5%
375
 
0.4%
304
 
0.3%
39.074
 
0.3%
454
 
0.3%
514
 
0.3%
553
 
0.2%
1503
 
0.2%
42.073
 
0.2%
Other values (1055)1167
96.5%
ValueCountFrequency (%)
0.511
 
0.1%
1.941
 
0.1%
1.956
0.5%
2.381
 
0.1%
3.51
 
0.1%
4.641
 
0.1%
4.721
 
0.1%
5.021
 
0.1%
5.51
 
0.1%
5.511
 
0.1%
ValueCountFrequency (%)
510.261
0.1%
450.951
0.1%
441.851
0.1%
390.611
0.1%
386.11
0.1%
3781
0.1%
376.51
0.1%
354.491
0.1%
3501
0.1%
343.081
0.1%

Electricity Price 7d
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1065
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.58015715
Minimum0.51
Maximum510.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:06.646690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile20.7
Q136.53
median49.89
Q388.61
95-th percentile238.044
Maximum510.26
Range509.75
Interquartile range (IQR)52.08

Descriptive statistics

Standard deviation73.38094199
Coefficient of variation (CV)0.9221009937
Kurtosis3.665363304
Mean79.58015715
Median Absolute Deviation (MAD)16.71
Skewness1.925258256
Sum96212.41
Variance5384.762648
MonotonicityNot monotonic
2022-12-19T12:42:06.736877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
406
 
0.5%
1.956
 
0.5%
375
 
0.4%
304
 
0.3%
454
 
0.3%
39.074
 
0.3%
354
 
0.3%
45.13
 
0.2%
50.293
 
0.2%
553
 
0.2%
Other values (1055)1167
96.5%
ValueCountFrequency (%)
0.511
 
0.1%
1.941
 
0.1%
1.956
0.5%
2.381
 
0.1%
3.51
 
0.1%
4.641
 
0.1%
4.721
 
0.1%
5.021
 
0.1%
5.51
 
0.1%
5.511
 
0.1%
ValueCountFrequency (%)
510.261
0.1%
450.951
0.1%
441.851
0.1%
390.611
0.1%
386.11
0.1%
3781
0.1%
376.51
0.1%
354.491
0.1%
3501
0.1%
343.081
0.1%

Gas Price
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct310
Distinct (%)86.6%
Missing851
Missing (%)70.4%
Infinite0
Infinite (%)0.0%
Mean15.25851955
Minimum7.8
Maximum27.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:06.827467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7.8
5-th percentile10.2175
Q112.8025
median14.165
Q317.09
95-th percentile24.3905
Maximum27.94
Range20.14
Interquartile range (IQR)4.2875

Descriptive statistics

Standard deviation3.993439128
Coefficient of variation (CV)0.2617186493
Kurtosis0.8339706174
Mean15.25851955
Median Absolute Deviation (MAD)1.79
Skewness1.079255339
Sum5462.55
Variance15.94755607
MonotonicityNot monotonic
2022-12-19T12:42:06.924079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.553
 
0.2%
13.523
 
0.2%
13.992
 
0.2%
9.132
 
0.2%
10.752
 
0.2%
11.82
 
0.2%
14.592
 
0.2%
13.192
 
0.2%
172
 
0.2%
15.072
 
0.2%
Other values (300)336
 
27.8%
(Missing)851
70.4%
ValueCountFrequency (%)
7.81
0.1%
8.381
0.1%
8.441
0.1%
8.841
0.1%
8.931
0.1%
8.951
0.1%
9.041
0.1%
9.091
0.1%
9.132
0.2%
9.231
0.1%
ValueCountFrequency (%)
27.941
0.1%
27.121
0.1%
27.041
0.1%
26.651
0.1%
26.381
0.1%
26.261
0.1%
25.831
0.1%
25.771
0.1%
25.731
0.1%
25.641
0.1%

Gas Price 24h
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1014
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.38356493
Minimum4.17
Maximum224.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:07.014560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.17
5-th percentile6.198
Q111.71
median16.24
Q334.87
95-th percentile97.8
Maximum224.38
Range220.21
Interquartile range (IQR)23.16

Descriptive statistics

Standard deviation33.1182748
Coefficient of variation (CV)1.055274469
Kurtosis3.741255987
Mean31.38356493
Median Absolute Deviation (MAD)6.75
Skewness1.916117177
Sum37942.73
Variance1096.820126
MonotonicityNot monotonic
2022-12-19T12:42:07.082372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.023
 
0.2%
14.883
 
0.2%
13.183
 
0.2%
16.13
 
0.2%
13.343
 
0.2%
13.523
 
0.2%
10.823
 
0.2%
18.563
 
0.2%
14.763
 
0.2%
17.733
 
0.2%
Other values (1004)1179
97.5%
ValueCountFrequency (%)
4.171
0.1%
4.231
0.1%
4.62
0.2%
4.91
0.1%
4.941
0.1%
5.021
0.1%
5.071
0.1%
5.081
0.1%
5.121
0.1%
5.172
0.2%
ValueCountFrequency (%)
224.381
0.1%
213.61
0.1%
193.111
0.1%
182.961
0.1%
181.541
0.1%
172.121
0.1%
169.391
0.1%
162.171
0.1%
161.981
0.1%
154.761
0.1%

Gas Price 7d
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1013
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.09038875
Minimum4.17
Maximum224.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2022-12-19T12:42:07.151743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.17
5-th percentile6.198
Q111.71
median16.24
Q334.13
95-th percentile97.8
Maximum224.38
Range220.21
Interquartile range (IQR)22.42

Descriptive statistics

Standard deviation32.91052519
Coefficient of variation (CV)1.05854338
Kurtosis3.949516276
Mean31.09038875
Median Absolute Deviation (MAD)6.75
Skewness1.958777839
Sum37588.28
Variance1083.102668
MonotonicityNot monotonic
2022-12-19T12:42:07.218612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.533
 
0.2%
14.763
 
0.2%
8.143
 
0.2%
18.563
 
0.2%
13.343
 
0.2%
16.243
 
0.2%
14.333
 
0.2%
13.33
 
0.2%
16.023
 
0.2%
11.613
 
0.2%
Other values (1003)1179
97.5%
ValueCountFrequency (%)
4.171
0.1%
4.231
0.1%
4.62
0.2%
4.91
0.1%
4.941
0.1%
5.021
0.1%
5.071
0.1%
5.081
0.1%
5.121
0.1%
5.172
0.2%
ValueCountFrequency (%)
224.381
0.1%
213.61
0.1%
193.111
0.1%
182.961
0.1%
181.541
0.1%
172.121
0.1%
169.391
0.1%
162.171
0.1%
161.981
0.1%
154.761
0.1%

Interactions

2022-12-19T12:42:03.158911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:49.670393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.612257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:51.546251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.683827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.679579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.611041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.690669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.661140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:57.579906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:58.492866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:59.540633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.454360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.378136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.279312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.220081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:49.737132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.674595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:51.721859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.755463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.746707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.677339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.758199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.723907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:57.644373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:58.555569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:59.603416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.517505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.437946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.340762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.275768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:49.796882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.734821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:51.786500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.820970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.807701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.738888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.820075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.780450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:57.706212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:58.612026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:59.660246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.573998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.498317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.395812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.339269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:49.864686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.803775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:51.860973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.894216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.875451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.807121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.889591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.844922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:57.771350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:58.676138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:59.724436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.638914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.559883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.458719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.401198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:49.930192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.870578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:51.934949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.963488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.941718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.999452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.958938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.908528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:57.833502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:58.739235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:59.788824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.702972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.621756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.520631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.640051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:49.994457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.935307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.004350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.028837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.004345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.063134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.024350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:56.969498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:57.895561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:58.951583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:59.849766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.763926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.679082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.579973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.701021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:50.060610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:51.002322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.077818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T12:41:53.359155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:54.314579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:55.384770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T12:41:51.487951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:52.618941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:41:53.615593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T12:41:59.484680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:00.396988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:01.322410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:02.216406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T12:42:03.104686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-19T12:42:07.281185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-19T12:42:07.392906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-19T12:42:07.503474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-19T12:42:07.616533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-19T12:42:04.270045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-19T12:42:04.420633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-19T12:42:04.501300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-19T12:42:04.545730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datetimeConsumptionSolar GenerationWind GenerationHydropower GenerationPump Hydro GenerationCombined Cycle GenerationNuclear GenerationTotal emissions (tCO2eq)tCO2/MWhElectricity PriceElectricity Price 24hElectricity Price 7dGas PriceGas Price 24hGas Price 7d
02019-01-08805391.716757.9171527.755914.48632.8120908.1170577.3221495.4910.27259.5959.5966.8825.4625.2624.45
12019-01-09804813.216267.5240796.641528.82160.883088.1170472.3211748.4180.25054.0959.5961.7426.2625.4624.87
22019-01-10812677.017381.3254571.249847.86312.5100567.0170458.8198839.7850.22958.6054.0962.0125.0126.2624.67
32019-01-11824726.717521.0208192.756627.65181.5132320.7170326.7202390.5280.23660.8458.6067.2025.8325.0126.07
42019-01-12732330.316823.8178126.032378.8171.085428.6170420.6170297.8630.23067.1860.8471.6824.8425.8325.21
52019-01-13685968.716659.0179078.236493.33610.472341.1170055.1164868.1130.23057.4867.1863.7524.6524.8424.86
62019-01-14797089.616963.2176051.246252.11417.7140301.3170352.4229900.0380.27560.0057.4859.5925.7324.6525.26
72019-01-15804682.515296.656366.171796.74996.3194768.8170324.9265329.5030.33060.0060.0059.5927.1225.7325.46
82019-01-16807008.813647.255128.076225.33168.8192815.7170387.7269859.0510.33270.1660.0054.0927.9427.1226.26
92019-01-17811027.49787.594843.762607.04504.7161302.4170529.2258317.3610.32967.3670.1658.6026.3827.9425.01

Last rows

datetimeConsumptionSolar GenerationWind GenerationHydropower GenerationPump Hydro GenerationCombined Cycle GenerationNuclear GenerationTotal emissions (tCO2eq)tCO2/MWhElectricity PriceElectricity Price 24hElectricity Price 7dGas PriceGas Price 24hGas Price 7d
11992022-04-21654314.395522.0238029.751202.46587.854681.5165912.376981.9140.102154.50125.01232.45NaN74.1088.14
12002022-04-22656665.638110.6192322.452241.97950.581128.7160081.791964.5680.137216.06154.50214.90NaN82.8580.52
12012022-04-23585714.374847.9358213.832675.79753.932779.7145673.362311.2840.082180.21216.06222.43NaN83.7677.31
12022022-04-24531093.4100440.9189947.737424.78863.581333.8134852.179587.4810.117144.00180.21160.22NaN77.8573.85
12032022-04-25618297.0108105.938786.749228.52676.0159859.6119952.6124532.6910.189267.43144.0095.90NaN81.2667.07
12042022-04-26641312.981619.063729.653010.11625.4202367.1120648.2144634.3620.207237.76267.43120.00NaN85.5168.69
12052022-04-27652403.068119.244722.560676.0NaN209518.8120739.8149748.1500.219211.96237.76125.01NaN84.7774.10
12062022-04-28649448.270969.988910.273743.9NaN133254.5120696.0127193.2730.190219.39211.96154.50NaN83.1482.85
12072022-04-29635568.0105498.086633.080855.8NaN127220.2123233.9124025.6670.173206.05219.39216.06NaN88.5883.76
12082022-04-30554742.8100820.6115253.366424.9NaN83544.2136806.484387.1800.124204.78206.05180.21NaN81.3277.85